{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UCI Condition Monitoring dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from pathlib import Path\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (20, 10)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring and\n",
      "saving processed datasets in /home/projects/akita/data/benchmark-data/data-processed\n"
     ]
    }
   ],
   "source": [
    "dataset_collection_name = \"UCI Condition Monitoring\"\n",
    "source_folder = Path(data_raw_folder) / \"UCI ML Repository/Condition monitoring\"\n",
    "target_folder = Path(data_processed_folder)\n",
    "\n",
    "from pathlib import Path\n",
    "print(f\"Looking for source datasets in {source_folder.absolute()} and\\nsaving processed datasets in {target_folder.absolute()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read label information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/profile.txt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-5644c4893f79>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Cooler condition\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Valve condition\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Internal pump leakage\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Hydraulic accumulator\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Stable\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_profile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_folder\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m\"profile.txt\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'\\t'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf_profile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf_profile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/timeeval/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    603\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/timeeval/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/timeeval/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    812\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 814\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    815\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/timeeval/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1043\u001b[0m             )\n\u001b[1;32m   1044\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1045\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1046\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1047\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/timeeval/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1860\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1861\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1862\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1863\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1864\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"storage_options\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"encoding\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"memory_map\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"compression\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/timeeval/lib/python3.9/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m   1355\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1356\u001b[0m         \"\"\"\n\u001b[0;32m-> 1357\u001b[0;31m         self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1358\u001b[0m             \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1359\u001b[0m             \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/timeeval/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    637\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 639\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    640\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/profile.txt'"
     ]
    }
   ],
   "source": [
    "columns = [\"Cooler condition\", \"Valve condition\", \"Internal pump leakage\", \"Hydraulic accumulator\", \"Stable\"]\n",
    "df_profile = pd.read_csv(source_folder / \"profile.txt\", sep='\\t', header=None)\n",
    "df_profile.columns=columns\n",
    "df_profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/PS6.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/PS1.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/PS2.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/TS1.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/PS3.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/TS3.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/FS1.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/EPS1.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/TS2.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/CP.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/VS1.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/CE.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/FS2.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/TS4.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/PS5.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/PS4.txt'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Condition monitoring/SE.txt')]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ignore_files = [\"description.txt\", \"documentation.txt\", \"profile.txt\"]\n",
    "sensor_files = [f for f in source_folder.glob(\"*.txt\") if f.name not in ignore_files]\n",
    "sensor_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fs1 = pd.read_csv(source_folder / \"FS1.txt\", sep='\\t', header=None).T\n",
    "df_fs1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_fs1.plot(y=range(5), figsize=(20,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval",
   "language": "python",
   "name": "timeeval"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
